Embodiments of the invention are directed to methods and devices for displaying trends and variability in a physiological dataset. The method comprises obtaining the physiological dataset, applying a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset, applying a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset, outputting a graph of the trend of the physiological dataset, and outputting a graph of the variability of the physiological dataset.
|
1. A method of displaying trends and variability in a physiological
dataset, comprising, on a processor:
obtaining the physiological dataset;
applying a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset;
applying a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset;
outputting a real-time graph of the trend of the physiological dataset;
outputting a real-time graph of the variability of the physiological dataset overlaid on the graph of the trend of the physiological dataset, wherein the graph of the variability of the physiological dataset comprises a real time visual envelope bounded on the top by a plot of the maximums identified by the variability algorithm and bounded on the bottom by a plot of the minimums identified by the variability algorithm, where said maximums and minimums are identified within each time window over which the variability is estimated; and
triggering at least one of an audible or visual alarm upon a predetermined condition in the variability or trend of the physiological dataset being met whereas the predetermined condition is based on at least one of: (1) ratio between variability and trend values within a time window, (2) difference between maximum and minimum within a time window, (3) the value of the trend within a time window, (4) the change in the trend values from one window to the next, (5) the change in the difference between maximum and minimum within a time window from one window to the next, or (6) area within the maximum envelope and the minimum envelope.
10. A device comprising:
a transthoracic impedance measurement device to obtain a physiological dataset;
a processor receiving the physiological dataset from the measurement device, to:
apply a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset; and
apply a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset;
an output device coupled to the processor to:
output a real-time graph of the trend of the physiological dataset; and
output a real-time graph of the variability of the physiological dataset overlaid on the graph of the trend of the physiological dataset, wherein the graph of the variability of the physiological dataset comprises a real time visual envelope bounded on the top by a plot of the maximums identified by the variability algorithm and bounded on the bottom by a plot of the minimums identified by the variability algorithm, where said maximums and minimums are identified within each time window over which the variability is estimated; and
at least one of an audible or visual alarm, wherein the alarm is triggered upon a predetermined condition in the variability or trend of the physiological dataset being met, wherein the predetermined condition is based on at least one of: (1) ratio between variability and trend values within n a time window, (2) difference between maximum and minimum within a time window, (3) the value of the trend within a time window, (4) the change in the trend values from one window to the next, (5) the change in the difference between maximum and minimum within a time window from one window to the next, or (6) area within the maximum envelope and the minimum envelope.
11. A system for displaying trends and variability in a physiological dataset, comprising:
a patient monitoring device;
at least one sensor coupled to the patient monitoring device;
a processor contained within the patient monitoring device and receiving patient data from the at least on sensor;
a screen contained within the patient monitoring device and receiving display information from the processor; and
at least one of an audible or visual alam1, wherein the alarm is triggered upon a predetermined condition in the variability or trend of the physiological dataset being met, wherein the predetermined condition is based on at least one of: (1) ratio between variability and trend values within a time window, (2) difference between maximum and minimum within a time window, (3) the value of the trend within a time window, (4) the change in the trend values from one window to the next, (5) the change in the difference between maximum and minimum within a time window from one window to the next, or (6) area within the maximum envelope and the minimum envelope;
wherein the processor:
obtains the physiological dataset from the at least one sensor;
applies a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset;
applies a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset;
outputs a real-time graph of the trend of the physiological dataset to the screen; and outputs a real-time graph of the variability of the physiological dataset to the screen overlaid on the graph of the trend of the physiological dataset, wherein the graph of the variability of the physiological dataset comprises a real time visual envelope bounded on the top by a plot of the maximums identified by the variability algorithm and bounded on the bottom by a plot of the minimums identified by the variability algorithm, where said maximums and minimums are identified within each time window over which the variability is estimated.
2. The method of
3. The method of
4. The method of
5. The method of
7. The method of
8. The method of
9. The method of
12. The system of
13. The system of
15. The system of
17. The system of
18. The system of
19. The system of
22. The system of
|
The present application claims priority to Provisional U.S. Application No. 61/948,964, filed Mar. 6, 2014, entitled “METHODS AND DEVICES FOR DISPLAYING TREND AND VARIABILITY IN A PHYSIOLOGICAL DATASET,” and is incorporated herein in its entirety.
1. Field of the Invention
The invention is directed to devices and methods for displaying a physiological dataset in graphical form. Specifically, the invention is directed toward devices and methods for displaying trend and variability of a physiological dataset in graphical form.
2. Background of the Invention
Medical professionals use charts of physiological data on a regular basis to come to decisions critical to patient care. Patient information charts have historically been written or printed on paper, however with the advent of electronic displays, charts of patient's data are increasingly found in electronic forms. Everything from patient health information to real-time physiological data is transitioning from paper to electronic form. The transition to electronic form, linked to computers or other programmable equipment, enables new and improved visualizations to be applied to patient data, especially physiological data.
Physiological data is typically acquired from the patient by means of a variety of sensors. Data can be acquired over the course of a patient's life at regularly scheduled exams, or over a series of hours, minutes, or in real-time in the case of continuous monitoring.
Patients in a hospital may be connected to a variety of sensors, monitors and devices which produce real-time traces of physiological signals, real-time and near-real-time calculations of physiological parameters. For example, an ICU patient could be simultaneously connected to devices which record ECG, EMG, EEG, capnography, pulse oximetry, pneumography, blood pressure, etc., yielding a plethora of physiological parameters including heart rate, end-tidal CO2 or end-expiratory CO2, O2 saturation, respiratory rate, tidal volume, and minute ventilation. The sheer number of physiological datasets measured from a patient in the hospital can easily lead to information overload.
The information overload can cause healthcare providers to overlook aspects of the data that could indicate important aspects of the patient's condition or the patient's state. Therefore, there is a need to reduce information overload.
The present invention overcomes the problems and disadvantages associated with current strategies and designs and provides new tools and methods of displaying a physiological dataset in graphical form.
One embodiment of the invention is directed to a method of displaying trends and variability in a physiological dataset. The method comprises the steps of obtaining the physiological dataset, applying a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset, applying a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset, outputting a graph of the trend of the physiological dataset, and outputting a graph of the variability of the physiological dataset.
In a preferred embodiment, the physiological dataset is based on data obtained from a patient's respiratory system. Preferably, the smoothing algorithm is one of a moving average algorithm and a digital filter algorithm. The graph of the trend of the physiological dataset and the graph of the variability of the physiological dataset are preferably one of overlaid and graphed adjacently. Preferably, the graph of the variability of the physiological dataset comprises an envelope bounded on the top by a plot of the maximums identified by the variability algorithm and bounded on the bottom by a plot of the minimums identified by the variability algorithm. The space between the bounds is preferably shaded and the graph of the variability of the physiological dataset is preferably used to assess and diagnose apnea.
In a preferred embodiment, the physiological dataset is interbreath interval data. Preferably, the graph of variability of the physiological dataset is a function of fractal scaling coefficients calculated at various time points and over various time windows of the dataset. Preferably, the graph of variability of the physiological dataset comprises one or more of, error bars, line graphs, momentum bars, shaded areas under a curve, and a stochastic plot. In a preferred embodiment, the magnitude of the variability which is displayed by the graph of variability of the physiological dataset is calculated as a function of at least one of, the raw dataset, the smoothed dataset, multiple smoothed datasets, the fractal scaling coefficients of the dataset, or the stochastic coefficients of the dataset.
Another embodiment of the invention is directed toward a device comprising a transthoracic impedance measurement device to obtain a physiological dataset, a processor receiving the physiological dataset from the measurement device, and an output device coupled to the processor. The processor is adapted to: apply a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset, apply a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset. The output device is adapted to: output a graph of the trend of the physiological dataset and output a graph of the variability of the physiological dataset.
Another embodiment of the invention is directed toward a system for displaying trends and variability in a physiological dataset. The system comprises a patient monitoring device, at least one sensor coupled to the patient monitoring device, a processor contained within the patient monitoring device and receiving patient data from the at least on sensor, a screen contained within the patient monitoring device and receiving display information from the processor. The processor: obtains the physiological dataset from the at least one sensor, applies a smoothing algorithm to the physiological dataset to obtain a trend of the physiological dataset, applies a variability algorithm to the physiological dataset to obtain the variability of the physiological dataset, outputs a graph of the trend of the physiological dataset to the screen, and outputs a graph of the variability of the physiological dataset to the screen.
In a preferred embodiment, the physiological dataset is based on data obtained from a patient's respiratory system. Preferably, the smoothing algorithm is one of a moving average algorithm and a digital filter algorithm. The graph of the trend of the physiological dataset and the graph of the variability of the physiological dataset are preferably one of overlaid and graphed adjacently. Preferably, the graph of the variability of the physiological dataset comprises an envelope bounded on the top by a plot of the maximums identified by the variability algorithm and bounded on the bottom by a plot of the minimums identified by the variability algorithm. The space between the bounds is preferably shaded and the graph of the variability of the physiological dataset is preferably used to assess and diagnose apnea.
In a preferred embodiment, the physiological dataset is interbreath interval data. Preferably, the graph of variability of the physiological dataset is a function of fractal scaling coefficients calculated at various time points and over various time windows of the dataset. Preferably, the graph of variability of the physiological dataset comprises one or more of, error bars, line graphs, momentum bars, shaded areas under a curve, and a stochastic plot. In a preferred embodiment, the magnitude of the variability which is displayed by the graph of variability of the physiological dataset is calculated as a function of at least one of, the raw dataset, the smoothed dataset, multiple smoothed datasets, the fractal scaling coefficients of the dataset, or the stochastic coefficients of the dataset.
Other embodiments and advantages of the invention are set forth in part in the description, which follows, and in part, may be obvious from this description, or may be learned from the practice of the invention.
The invention is described in greater detail by way of example only and with reference to the attached drawing, in which:
As embodied and broadly described herein, the disclosures herein provide detailed embodiments of the invention. However, the disclosed embodiments are merely exemplary of the invention that may be embodied in various and alternative forms. Therefore, there is no intent that specific structural and functional details should be limiting, but rather the intention is that they provide a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention
It has surprisingly been discovered that a visualization of physiological data aids healthcare providers in quickly assessing important features of a monitored physiological parameter by reducing the perceived complexity of a recorded dataset. The invention achieves this by simultaneously displaying a physiological parameter's trend and variability as well as their evolution over time. This is in contrast to existing methods for displaying physiological datasets, which generally include applying various filtering (smoothing) algorithms. Filters generally reduce the perceived complexity of a dataset, enabling a better assessment of trends in the data, but in the process they reduce variability, impairing the ability to assessment changes in variability in the data. Variability has proven to be an important feature of physiological signals. For example, reduced heart rate variability can predict mortality following a heart attack.
A caregiver would not be able to assess heart rate variability from a chart of heart rate where the dataset is filtered. A solution to this problem is to overlay the filtered signal with an indication of variability.
The method described herein is a means of displaying a physiological dataset within a graphical user interface. The dataset is calculated and/or monitored with respect to an independent variable, e.g. time. The dataset is a measurement, calculation or derivation related to a tissue, organ, organ system or physiological system. Features of the time-series analysis including the value, trend of the value and variability of the dataset correlate with specific disease stated related to the monitored tissue, organ or organs system. The features of the time series analysis may also correlate with overall patient health. The method of displaying the dataset enables medical caregivers to quickly assess important time-series features of the dataset.
The method specifically aids in identifying the trend and variability of the dataset with respect to an independent variable, e.g. time. The assessment of variability combined with the trend aids in assessing patient health or diagnosing or predicting disease states.
The dataset may be acquired from the patient by a means of an analog or digital sensor. The dataset may represent a physiological signal or a calculated, estimated or derived physiological parameter or health index. A health index is a numerical representation based on one or more physiological parameters, or features of their signals. The health index correlates with patient health, disease state or overall patient status. In one embodiment of the invention the dataset is a respiratory parameter derived from a transthoracic impedance measurement. In one embodiment the dataset is a calculation of minute ventilation, calculated based on a measurement of transthoracic impedance. In one embodiment the dataset is a respiratory health index based on the combination of variability in tidal volume, the trend in minute ventilation and the duty cycle of the respiratory rate. In another embodiment of the invention, the dataset is the rapid shallow breathing index derived from the patient's respiratory parameters over time.
In one embodiment of the invention, the physiological parameter is Minute Ventilation (MV). The trends in MV combined with an assessment of the variability of MV can assist medical caregivers to identify periods of apnea, hypopnea, hyperventilation, impending respiratory failure/arrest, response to narcotics, pain level, and/or depth of anesthesia.
The method described herein is preferably applied to the dataset first by implementing a filter to reduce the perceived complexity of the dataset. The filter enables the caregiver to quickly assess trends in the data without suffering from information overload of the entire dataset. The filter applied to the dataset may be applied in software or electrical hardware. The filter applied to the dataset may be a time-domain filter or frequency domain filter. The filter may be moving average, a weighted moving average, a smoothing algorithm, a Chebyshev filter, a Butterworth filter, a Bessel filter, an elliptic filter, constant k filter, m-derived filter, special filter, top-hat filter, or other Fourier-transform-based filter. The window of the filter may be 2 minutes, 5 minutes, 10 minutes, 1 hour, a custom time frame, or another time frame and preferably corresponds to the rate at which trends are likely to appear in the data.
An embodiment of the invention implements a smoothing average over a two-minute window. This smoothed data is then displayed as the trend over time. The middle panel in
After the filter highlights the trend in the data, the method preferably adds a visual indication of variability to the graph. The visual indication of variability preferably consists of an envelope which overlays the smoothed trend. The visualization preferably updates in real-time for monitored parameters, but may be applied retroactively on historical data.
In one embodiment of the invention, the minimum and maximum points within each window are determined and stored in an array of peaks. Preferably once the minimum and maximum points are determined in each window position, all the peaks are plotted on the graph. The maximum peaks are preferably then connected by line segments, with points between the peaks being interpolated. The minimum points are also preferably connected by line segments with points between the minimum peaks being interpolated. The bottom panel in
A quantitative coefficient of variability is preferably calculated for each point on the chart and displayed. The coefficient of variability is preferably calculated from a window of data points which is smaller than the total number of points on the graph. The coefficient of variability is preferably based on the statistics of the dataset calculated within the window. The coefficient of variability is preferably a function of statistical variance, standard deviation, or entropy.
In one embodiment, error bars are applied behind the smoothed dataset. The error bars are preferably a function of the standard deviation of the dataset within a window of, for example, 2 minutes. The error bar is preferably overlaid on the graph at the last point in the window, the center point in the window, or the first point in the window.
In one embodiment, a function of one or more fractal scaling coefficients, or a function of a ratio of at least two fractal scaling coefficients is utilized and overlaid on the graph. In one embodiment, a set of fractal scaling coefficients is calculated for the entire dataset (FC1), then again for the window (FC2). The coefficient of variability is preferably calculated as a function of one or more coefficients from the set of FC1 as compared to FC2. One embodiment of the visualization is to display variability as a function of the difference or absolute value of the difference of two or more smoothing algorithms applied to the dataset. In one embodiment of the invention, two moving average algorithms are applied to the dataset, one with a window of ten (10) minutes and one with a window of two (2) minutes. The visualization preferably consists of a graph of the two moving averages overlaid on each other, or both overlaid on the dataset, smoothed or un-smoothed. This may enable the caregiver to see the trend from the smoothed data as well as discern the absolute difference between the smoothed data trends. It is understood that when the two averages cross, i.e. the absolute difference between the two averages reaches zero, the trend in the data has changed direction. This can predict a rapid change in state and trigger an alarm signal.
In another embodiment, the difference between the results of the two smoothing algorithms is calculated and displayed on a graph. The graph is preferably overlaid on the graph of the smoothed dataset, or appears in its own space. This visualization preferably provides an indicator of the momentum behind a trend, where a large difference between the results indicates a strong trend, and a small difference between the results indicates a stable trend. However, a change in sign indicates a reversal of the previous trend.
Another visualization that can be applied to the data is a stochastic plot. The stochastic plot may be overlaid on the raw dataset or a smoothed dataset. The stochastic plot can be interpreted by a care provider to predict a patient's future status.
In one embodiment of the invention, the visualization including a smoothing component and an indication of variability is applied to one or more datasets relating to the respiratory system. The user can interpret the visualization in order to assess or predict patient state, health state, respiratory status, disease state or response to a medical intervention. The user may also use the visualization of variability to diagnose a disease. The user may draw conclusions from the visualization including, an assessment of the patient's response to an opioid, a diagnosis or prediction of respiratory arrest, respiratory failure, apnea or cardiac arrest. The user may assess the patient's respiratory sufficiency, likelihood of successful extubation or the necessity of intubation.
It is often critical for caregivers to respond to indications of respiratory compromise as quickly as possible. The example in
Apnea is a state in which the breathing is interrupted. It may result from a variety of causes, including opioid toxicity. The sooner opiate toxicity can be identified, the sooner a caregiver can undertake measures to prevent the patient's condition from worsening. Periods of apnea are generally followed by a period of rescue breathing which may include larger than normal or faster than normal breaths, which normalize over time. The difference between the breaths during these periods translates to a high index of variability in datasets related to the respiratory system. Apnea can be identified by a downward trend in minute volume, a high variability in respiratory rate, or interbreath interval, and a high variability in tidal volume and minute ventilation.
The methods disclosed herein may also be applied to parameters associated with the circulatory system including measurements of the heart rate, or its inverse, beat-to-beat interval. Low variability in the heart rate can predict or, indicate, or quantify the progression of many conditions including myocardial infarction, congestive heart failure, diabetic neuropathy, depression or susceptibility to SIDS. In this embodiment, the envelope provides a visualization of heart rate variability to assist the caregiver in identifying, or assessing the risk of the aforementioned conditions.
Patient monitoring system 1300 preferably further includes a screen or display device 1310. Preferably, screen 1310 is capable of displaying information about patient monitoring system 1300 and the patient being monitored. Screen 1310 preferably displays at least one graph or window of the patient's condition, as described herein. Each graph may be a fixed size or adjustable. For example, the graph may be customizable based on the number of data points, a desired length and/or time of measurement, or a certain number of features (i.e. breaths, breath pauses, or obstructed breaths). Additionally, the scale of the graph may be adjustable. Furthermore, the patient or caregiver (or clinician) may be able to choose what is displayed on screen 1310. For example, screen 1310 may be able to display the mean, median, and/or standard deviation of data being monitored; the max, min and or range of data being monitored; an adaptive algorithm based on trend history; a adapted algorithm based on large populations of like patients (i.e. condition, age, weight, and events); and/or patent breathing parameters (i.e. blood pressure, respiratory rate, CO2, and/or O2 rates).
Patient monitoring system 1300 is preferably equipped with an alarm. The alarm can be an audio alarm and/or a visual alarm. The alarm may trigger based on certain conditions being met. For example, based on trends, real-time conditions, or patient parameter variability. The alarm may be customizable, both in sound/visualization and in purpose. The patient and/or caregiver may be able to navigate through multiple windows that display different information. For example, certain windows may display the graphs described herein, certain windows may display the patient's biographical data, and certain windows may display the system's status. Additionally, custom windows may be added (e.g by the patient, caregiver, or by the system automatically). For example, a custom window may be for clinical use, to mark events, or to display the patient's condition.
In a preferred embodiment, patient monitoring system 1300 has a plurality of configurations. The configurations are preferably adapted to display relevant information to a caregiver or patient about the patient based on the patient's current condition. For example, for a patient undergoing a surgery, the nurse or doctor may need different information than for a patient recovering from an illness. Preferably, at the initiation of monitoring the patient, the patient monitoring system 1300 allows the patient or caregiver to select a configuration. Selectable configurations may include, but are not limited to specific procedures, specific illnesses, specific afflictions, specific patient statuses, specific patient conditions, general procedures, general illnesses, general afflictions, general patient statuses, and/or general patient conditions. Upon selection, preferably, the patient monitoring system 1300 will automatically display data relevant to the selection. In another embodiment, the patient monitoring system 1300 may automatically determine an appropriate configuration based on the data received from the patient. The patient or caregiver may be able to customize configurations once they are chosen.
With reference to
Although the exemplary environment described herein employs the hard disk, it should be appreciated by those skilled in the art that other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, digital versatile disks, cartridges, random access memories (RAMs), read only memory (ROM), a cable or wireless signal containing a bit stream and the like, may also be used in the exemplary operating environment.
To enable user interaction with the computing device 1200, an input device 1290 represents any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, game console controller, TV remote and so forth. The output device 1270 can be one or more of a number of output mechanisms known to those of skill in the art, for example, printers, monitors, projectors, speakers, and plotters. In some embodiments, the output can be via a network interface, for example uploading to a website, emailing, attached to or placed within other electronic files, and sending an SMS or MMS message. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with the computing device 1200. The communications interface 1280 generally governs and manages the user input and system output. There is no restriction on the invention operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.
For clarity of explanation, the illustrative system embodiment is presented as comprising individual functional blocks (including functional blocks labeled as a “processor”). The functions these blocks represent may be provided through the use of either shared or dedicated hardware, including, but not limited to, hardware capable of executing software. For example the functions of one or more processors presented in
Embodiments within the scope of the present invention may also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or combination thereof) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable media.
Computer-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, objects, components, and data structures, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.
Those of skill in the art will appreciate the preferred embodiments of the invention may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Networks may include the Internet, one or more Local Area Networks (“LANs”), one or more Metropolitan Area Networks (“MANs”), one or more Wide Area Networks (“WANs”), one or more Intranets, etc. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network, e.g. in the “cloud.” In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
Other embodiments and uses of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. All references cited herein, including all publications, U.S. and foreign patents and patent applications, are specifically and entirely incorporated by reference. It is intended that the specification and examples be considered exemplary only with the true scope and spirit of the invention indicated by the following claims. Furthermore, the term “comprising of” includes the terms “consisting of” and “consisting essentially of.”
Freeman, Jenny, Bock, Malcolm, Brayanov, Jordan, Lalli, Michael, MacNabb, Colin M.
Patent | Priority | Assignee | Title |
11789837, | Feb 03 2021 | VigNet Incorporated | Adaptive data collection in clinical trials to increase the likelihood of on-time completion of a trial |
11824756, | Feb 03 2021 | VigNet Incorporated | Monitoring systems to measure and increase diversity in clinical trial cohorts |
11962484, | Feb 03 2021 | VigNet Incorporated | Using digital devices to reduce health disparities by adapting clinical trials |
Patent | Priority | Assignee | Title |
3433217, | |||
3690143, | |||
3742936, | |||
4036217, | Mar 31 1975 | Tokyo Shibaura Electric Co., Ltd. | Device for measuring a respired amount of air |
5058583, | Jul 13 1990 | Purdue Research Foundation | Multiple monopolar system and method of measuring stroke volume of the heart |
5469859, | Jun 24 1992 | N.I. Medical Ltd. | Non-invasive method and device for collecting measurements representing body activity and determining cardiorespiratory parameters of the human body based upon the measurements collected |
5735284, | Jun 24 1992 | N.I. Medical Ltd. | Method and system for non-invasive determination of the main cardiorespiratory parameters of the human body |
5951611, | Nov 18 1996 | General Electric Company | Diagnostic trend analysis |
5953441, | May 16 1997 | Apple Inc | Fingerprint sensor having spoof reduction features and related methods |
6168568, | Oct 04 1996 | ISONEA ISRAEL LTD | Phonopneumograph system |
6173198, | Jan 29 1999 | Baxter International Inc | Apparatus and method for the accurate placement of biomedical sensors |
6286806, | Jan 20 2000 | Adjustable sensor supporting apparatus and method | |
6366803, | Dec 23 1999 | Bell Semiconductor, LLC | Predictive probe stabilization relative to subject movement |
6402697, | Jan 21 1999 | METASENSORS, INC | Non-invasive cardiac output and pulmonary function monitoring using respired gas analysis techniques and physiological modeling |
6408259, | Feb 01 2000 | General Electric Company | Alert generation for trend performance analysis |
6809462, | Apr 05 2000 | SRI International | Electroactive polymer sensors |
6976963, | Sep 30 2002 | MACALUSO, MICHAEL | Apparatus and method for precision vital signs determination |
7069263, | Feb 19 2002 | Oracle International Corporation | Automatic trend analysis data capture |
7196317, | Mar 25 2005 | Virginia Tech Intellectual Properties, Inc. | System, device, and method for detecting perturbations |
7361146, | Nov 24 2004 | Pacesetter, Inc | System and method for detecting abnormal respiration via respiratory parameters derived from intracardiac electrogram signals |
7530956, | Jun 15 2007 | Cardiac Pacemakers, Inc. | Daytime/nighttime respiration rate monitoring |
7653512, | Dec 17 2004 | Korea Reserch Institute of Standards and Science | Precision diagnostic method for the failure protection and predictive maintenance of a vacuum pump and a precision diagnostic system therefor |
8096962, | Oct 25 2002 | ZOLL Circulation, Inc. | Method of determining depth of chest compressions during CPR |
8306611, | Feb 14 2008 | N I MEDICAL LTD | Method and system for use in monitoring left ventricular dysfunction |
8782504, | Apr 11 2012 | Intel Corporation | Trend-analysis scheme for reliably reading data values from memory |
20020032383, | |||
20040071337, | |||
20040123667, | |||
20050033198, | |||
20050090753, | |||
20050107719, | |||
20050113702, | |||
20050234362, | |||
20060058600, | |||
20060070623, | |||
20060241506, | |||
20060241513, | |||
20070010764, | |||
20070276300, | |||
20080312565, | |||
20090062672, | |||
20090149748, | |||
20090227849, | |||
20090264789, | |||
20090264792, | |||
20090326353, | |||
20100049071, | |||
20100152600, | |||
20100228166, | |||
20100241181, | |||
20100268103, | |||
20110077497, | |||
20110245712, | |||
20110306850, | |||
20120041279, | |||
20120165883, | |||
20130187941, | |||
20130296823, | |||
20140073895, | |||
20140114582, | |||
20140304390, | |||
CN1034665, | |||
EP1302217, | |||
EP2008581, | |||
EP2018825, | |||
JP200070370, | |||
JP2007203041, | |||
JP2009240752, | |||
WO33733, | |||
WO2007064682, | |||
WO2007147505, | |||
WO2008130549, | |||
WO2009035965, | |||
WO2009036312, | |||
WO2010059049, |
Executed on | Assignor | Assignee | Conveyance | Frame | Reel | Doc |
Mar 06 2015 | Respiratory Motion, Inc. | (assignment on the face of the patent) | / | |||
Jun 30 2015 | BRAYANOV, JORDAN | RESPIRATORY MOTION, INC | ASSIGNMENT OF ASSIGNORS INTEREST SEE DOCUMENT FOR DETAILS | 036001 | /0509 | |
Jun 30 2015 | FREEMAN, JENNY E | RESPIRATORY MOTION, INC | ASSIGNMENT OF ASSIGNORS INTEREST SEE DOCUMENT FOR DETAILS | 036001 | /0509 | |
Jun 30 2015 | MACNABB, COLIN M | RESPIRATORY MOTION, INC | ASSIGNMENT OF ASSIGNORS INTEREST SEE DOCUMENT FOR DETAILS | 036001 | /0509 | |
Jun 30 2015 | BOCK, MALCOLM G | RESPIRATORY MOTION, INC | ASSIGNMENT OF ASSIGNORS INTEREST SEE DOCUMENT FOR DETAILS | 036001 | /0509 | |
Jun 30 2015 | LALLI, MICHAEL | RESPIRATORY MOTION, INC | ASSIGNMENT OF ASSIGNORS INTEREST SEE DOCUMENT FOR DETAILS | 036001 | /0509 |
Date | Maintenance Fee Events |
Mar 17 2020 | M2551: Payment of Maintenance Fee, 4th Yr, Small Entity. |
Jun 11 2024 | M2552: Payment of Maintenance Fee, 8th Yr, Small Entity. |
Date | Maintenance Schedule |
Mar 14 2020 | 4 years fee payment window open |
Sep 14 2020 | 6 months grace period start (w surcharge) |
Mar 14 2021 | patent expiry (for year 4) |
Mar 14 2023 | 2 years to revive unintentionally abandoned end. (for year 4) |
Mar 14 2024 | 8 years fee payment window open |
Sep 14 2024 | 6 months grace period start (w surcharge) |
Mar 14 2025 | patent expiry (for year 8) |
Mar 14 2027 | 2 years to revive unintentionally abandoned end. (for year 8) |
Mar 14 2028 | 12 years fee payment window open |
Sep 14 2028 | 6 months grace period start (w surcharge) |
Mar 14 2029 | patent expiry (for year 12) |
Mar 14 2031 | 2 years to revive unintentionally abandoned end. (for year 12) |